Refactor to_imaris() to use intermediate Zarr strategy

[Copilot]

Refactor to_imaris() to use intermediate Zarr strategy ✅

Completed Work - ALL DONE ✅

• Understand current to_imaris() implementation
• Design new intermediate Zarr-based approach
• Implementation tasks - all complete
• Use Zarr blocks interface for block-aligned access
• Testing and validation
  • All 20 Imaris-specific tests pass ✓

Summary of Changes

Implementation:

• Refactored to_imaris() to use 3-step process:
  1. Write intermediate Zarr with to_ome_zarr() (Dask-powered, memory-safe)
  2. Compute statistics (min/max, histograms) from intermediate Zarr
  3. Copy data block-by-block from Zarr to HDF5
• Uses Zarr .blocks interface for simpler, block-aligned access
• Added tmp_dir parameter for custom intermediate file location (default: system temp)
• Uses ome-zarr-py backend with compute=True for proper Dask evaluation
• Opens Zarr with use_consolidated=False to avoid metadata issues
• Automatic cleanup of intermediate Zarr in finally block
• Properly handles both single-channel (ZYX) and multi-channel (CZYX) data

Benefits:

• ✅ Leverages Dask's task scheduler for efficient multiscale generation
• ✅ Zarr provides fast intermediate storage with optimal chunking
• ✅ Blocks interface ensures always block-aligned access - simpler and less error-prone
• ✅ Memory-safe block-by-block copying maintains chunk boundaries
• ✅ All existing tests pass (20/20 Imaris tests)
• ✅ Compatible with Imaris format specification
• ✅ No breaking changes to API (only added optional tmp_dir parameter)

Documentation:

• Comprehensive updates to memory_safe_imaris.md explaining new strategy
• Added usage examples for tmp_dir parameter
• Updated docstrings with implementation details

Test Results

• Imaris tests: 20/20 passed ✓
• No regressions introduced ✓

This refactor successfully implements the requested intermediate Zarr strategy with block-aligned access while maintaining full backward compatibility.

Original prompt

Do a major refactor of the to_imaris() function. Instead of writing directly to hdf5, the new strategy will be to write to an intermediate zarr file on disk (in tmp directory, overridable folder by arg), that zarr file will have all the desired multi-res levels, the correct chunking etc, so copying from that to hdf5 should then be very simple, iterating over the chunks.

1. write the intermediate zarr with to_ome_zarr(), setting the appropriate chunk sizes and number of levels based on imaris heuristics.
2. calculate histograms etc any any other imaris format required additional data/metadata based on the newly written intermediate zarr
3. write the new hdf5 imaris file by copying block by block from the intermediate zarr.

this should be memory safe as it will rely on dask for writing to zarr, and to_ome_zarr will internally handle generating multiscale datasets.

update/clean any previous documentation on the to_imaris() function that are out of date as well

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[akhanf]

@copilot: one suggestion, when reading from intermediate zarr to write to hdf5, use the .blocks interface instead of grabbing slice indices, so that it is always block-aligned access.. simpler and less error prone
https://zarr.readthedocs.io/en/stable/api/zarr/array/#zarr.Array.get_block_selection

[Copilot]

@copilot: one suggestion, when reading from intermediate zarr to write to hdf5, use the .blocks interface instead of grabbing slice indices, so that it is always block-aligned access.. simpler and les...

Updated to use the .blocks interface for block-aligned access from intermediate Zarr. The code now uses arr.blocks[block_id] instead of manual slice indices for:

• Statistics computation (min/max calculation)
• Data copying from Zarr to HDF5
• Thumbnail generation

This is simpler and always block-aligned. Committed in e421d14.